A quantum artificial neural network for stock closing price prediction

In practice, stock market behavior is difficult to predict accurately because of its high volatility. To improve market forecasts, a method inspired by Elman neural network and quantum mechanics is presented. To render the network sensitive to dynamic information, the internal self-connection signal that is extremely useful for system modeling is introduced to the proposed technique. Double chains quantum genetic algorithm is employed to tune the learning rates. This model is validated by forecasting closing prices of six stock markets, the simulation results indicate that the proposed algorithm is feasible and effective. Accordingly, generalizing the method is deemed advantageous.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

Predicting stock market behavior accurately is challenging due to its high volatility. To enhance market forecasts, a method combining Elman neural network and quantum mechanics is proposed. By introducing the internally self-connected signal and employing the double chains quantum genetic algorithm to adjust learning rates, the network becomes sensitive to dynamic information. The model is validated by forecasting closing prices of six stock markets, demonstrating its feasibility and effectiveness, and suggesting potential for generalization.